LogisticRegression Model - Breast Cancer Dataset.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "78afe70f",
   "metadata": {},
   "source": [
    "## Problem Statement:\n",
    "#### I have implemented Feature Scaling, Selection, and Constant Column Removal in Machine Learning with the Breast Cancer Dataset from scikit-learn. you will learn how to preprocess and select features in machine learning using Python.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8586b7df",
   "metadata": {},
   "source": [
    "### Tasks:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31918ba0",
   "metadata": {},
   "source": [
    "##### 1. Import the necessary libraries: Start by importing the required libraries, including pandas, numpy, matplotlib, scikit-learn, and seaborn."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "96ba2291",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.feature_selection import VarianceThreshold\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "694508b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b88f954",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "82a6b25c",
   "metadata": {},
   "source": [
    "##### 2. Load the dataset: Load the breast cancer dataset into a pandas DataFrame using the load_breast_cancer() function from scikit-learn (sklearn.datasets())."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "746b7ec1",
   "metadata": {},
   "outputs": [],
   "source": [
    "breast_Cancer_Dataset = datasets.load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a010f658",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['data', 'target', 'frame', 'target_names', 'DESCR', 'feature_names', 'filename', 'data_module'])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_Cancer_Dataset.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bbec9ac7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "        8.758e-02],\n",
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       "        1.240e-01],\n",
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       "        7.039e-02]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_Cancer_Dataset['data']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e1575943",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error',\n",
       "       'fractal dimension error', 'worst radius', 'worst texture',\n",
       "       'worst perimeter', 'worst area', 'worst smoothness',\n",
       "       'worst compactness', 'worst concavity', 'worst concave points',\n",
       "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_Cancer_Dataset['feature_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b8a9558a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create DataFrame Contains data according to feature name column\n",
    "dataFrameBreastCancer = pd.DataFrame(breast_Cancer_Dataset['data'] , columns= breast_Cancer_Dataset['feature_names'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1acabcc9",
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_Cancer_Dataset['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4a69745e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFrameBreastCancer['Target'] = breast_Cancer_Dataset['target']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "651f4bc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'], dtype='<U9')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breast_Cancer_Dataset['target_names']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "34cb52dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFrameBreastCancer['Target Name'] = pd.Categorical(pd.Series(breast_Cancer_Dataset.target).map(lambda x: breast_Cancer_Dataset.target_names[x]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9e74b902",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
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       "      <th>...</th>\n",
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       "      <th>Target</th>\n",
       "      <th>Target Name</th>\n",
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       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
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       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0</td>\n",
       "      <td>malignant</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0</td>\n",
       "      <td>malignant</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0</td>\n",
       "      <td>malignant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0</td>\n",
       "      <td>malignant</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
       "      <td>0.10030</td>\n",
       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>0</td>\n",
       "      <td>malignant</td>\n",
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       "      <th>564</th>\n",
       "      <td>21.56</td>\n",
       "      <td>22.39</td>\n",
       "      <td>142.00</td>\n",
       "      <td>1479.0</td>\n",
       "      <td>0.11100</td>\n",
       "      <td>0.11590</td>\n",
       "      <td>0.24390</td>\n",
       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>0</td>\n",
       "      <td>malignant</td>\n",
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       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>0</td>\n",
       "      <td>malignant</td>\n",
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       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>0</td>\n",
       "      <td>malignant</td>\n",
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       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>0</td>\n",
       "      <td>malignant</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>1</td>\n",
       "      <td>benign</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0          17.99         10.38          122.80     1001.0          0.11840   \n",
       "1          20.57         17.77          132.90     1326.0          0.08474   \n",
       "2          19.69         21.25          130.00     1203.0          0.10960   \n",
       "3          11.42         20.38           77.58      386.1          0.14250   \n",
       "4          20.29         14.34          135.10     1297.0          0.10030   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0             0.27760         0.30010              0.14710         0.2419   \n",
       "1             0.07864         0.08690              0.07017         0.1812   \n",
       "2             0.15990         0.19740              0.12790         0.2069   \n",
       "3             0.28390         0.24140              0.10520         0.2597   \n",
       "4             0.13280         0.19800              0.10430         0.1809   \n",
       "..                ...             ...                  ...            ...   \n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     mean fractal dimension  ...  worst perimeter  worst area  \\\n",
       "0                   0.07871  ...           184.60      2019.0   \n",
       "1                   0.05667  ...           158.80      1956.0   \n",
       "2                   0.05999  ...           152.50      1709.0   \n",
       "3                   0.09744  ...            98.87       567.7   \n",
       "4                   0.05883  ...           152.20      1575.0   \n",
       "..                      ...  ...              ...         ...   \n",
       "564                 0.05623  ...           166.10      2027.0   \n",
       "565                 0.05533  ...           155.00      1731.0   \n",
       "566                 0.05648  ...           126.70      1124.0   \n",
       "567                 0.07016  ...           184.60      1821.0   \n",
       "568                 0.05884  ...            59.16       268.6   \n",
       "\n",
       "     worst smoothness  worst compactness  worst concavity  \\\n",
       "0             0.16220            0.66560           0.7119   \n",
       "1             0.12380            0.18660           0.2416   \n",
       "2             0.14440            0.42450           0.4504   \n",
       "3             0.20980            0.86630           0.6869   \n",
       "4             0.13740            0.20500           0.4000   \n",
       "..                ...                ...              ...   \n",
       "564           0.14100            0.21130           0.4107   \n",
       "565           0.11660            0.19220           0.3215   \n",
       "566           0.11390            0.30940           0.3403   \n",
       "567           0.16500            0.86810           0.9387   \n",
       "568           0.08996            0.06444           0.0000   \n",
       "\n",
       "     worst concave points  worst symmetry  worst fractal dimension  Target  \\\n",
       "0                  0.2654          0.4601                  0.11890       0   \n",
       "1                  0.1860          0.2750                  0.08902       0   \n",
       "2                  0.2430          0.3613                  0.08758       0   \n",
       "3                  0.2575          0.6638                  0.17300       0   \n",
       "4                  0.1625          0.2364                  0.07678       0   \n",
       "..                    ...             ...                      ...     ...   \n",
       "564                0.2216          0.2060                  0.07115       0   \n",
       "565                0.1628          0.2572                  0.06637       0   \n",
       "566                0.1418          0.2218                  0.07820       0   \n",
       "567                0.2650          0.4087                  0.12400       0   \n",
       "568                0.0000          0.2871                  0.07039       1   \n",
       "\n",
       "     Target Name  \n",
       "0      malignant  \n",
       "1      malignant  \n",
       "2      malignant  \n",
       "3      malignant  \n",
       "4      malignant  \n",
       "..           ...  \n",
       "564    malignant  \n",
       "565    malignant  \n",
       "566    malignant  \n",
       "567    malignant  \n",
       "568       benign  \n",
       "\n",
       "[569 rows x 32 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame containg Data of data,target and target columns...\n",
    "dataFrameBreastCancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dbf6eb91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "mean radius                0\n",
       "mean texture               0\n",
       "mean perimeter             0\n",
       "mean area                  0\n",
       "mean smoothness            0\n",
       "mean compactness           0\n",
       "mean concavity             0\n",
       "mean concave points        0\n",
       "mean symmetry              0\n",
       "mean fractal dimension     0\n",
       "radius error               0\n",
       "texture error              0\n",
       "perimeter error            0\n",
       "area error                 0\n",
       "smoothness error           0\n",
       "compactness error          0\n",
       "concavity error            0\n",
       "concave points error       0\n",
       "symmetry error             0\n",
       "fractal dimension error    0\n",
       "worst radius               0\n",
       "worst texture              0\n",
       "worst perimeter            0\n",
       "worst area                 0\n",
       "worst smoothness           0\n",
       "worst compactness          0\n",
       "worst concavity            0\n",
       "worst concave points       0\n",
       "worst symmetry             0\n",
       "worst fractal dimension    0\n",
       "Target                     0\n",
       "Target Name                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# this dataset does not contains any null values...\n",
    "dataFrameBreastCancer.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "61c082be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "      <td>569.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.127292</td>\n",
       "      <td>19.289649</td>\n",
       "      <td>91.969033</td>\n",
       "      <td>654.889104</td>\n",
       "      <td>0.096360</td>\n",
       "      <td>0.104341</td>\n",
       "      <td>0.088799</td>\n",
       "      <td>0.048919</td>\n",
       "      <td>0.181162</td>\n",
       "      <td>0.062798</td>\n",
       "      <td>...</td>\n",
       "      <td>25.677223</td>\n",
       "      <td>107.261213</td>\n",
       "      <td>880.583128</td>\n",
       "      <td>0.132369</td>\n",
       "      <td>0.254265</td>\n",
       "      <td>0.272188</td>\n",
       "      <td>0.114606</td>\n",
       "      <td>0.290076</td>\n",
       "      <td>0.083946</td>\n",
       "      <td>0.627417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.524049</td>\n",
       "      <td>4.301036</td>\n",
       "      <td>24.298981</td>\n",
       "      <td>351.914129</td>\n",
       "      <td>0.014064</td>\n",
       "      <td>0.052813</td>\n",
       "      <td>0.079720</td>\n",
       "      <td>0.038803</td>\n",
       "      <td>0.027414</td>\n",
       "      <td>0.007060</td>\n",
       "      <td>...</td>\n",
       "      <td>6.146258</td>\n",
       "      <td>33.602542</td>\n",
       "      <td>569.356993</td>\n",
       "      <td>0.022832</td>\n",
       "      <td>0.157336</td>\n",
       "      <td>0.208624</td>\n",
       "      <td>0.065732</td>\n",
       "      <td>0.061867</td>\n",
       "      <td>0.018061</td>\n",
       "      <td>0.483918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.981000</td>\n",
       "      <td>9.710000</td>\n",
       "      <td>43.790000</td>\n",
       "      <td>143.500000</td>\n",
       "      <td>0.052630</td>\n",
       "      <td>0.019380</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.106000</td>\n",
       "      <td>0.049960</td>\n",
       "      <td>...</td>\n",
       "      <td>12.020000</td>\n",
       "      <td>50.410000</td>\n",
       "      <td>185.200000</td>\n",
       "      <td>0.071170</td>\n",
       "      <td>0.027290</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.156500</td>\n",
       "      <td>0.055040</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.700000</td>\n",
       "      <td>16.170000</td>\n",
       "      <td>75.170000</td>\n",
       "      <td>420.300000</td>\n",
       "      <td>0.086370</td>\n",
       "      <td>0.064920</td>\n",
       "      <td>0.029560</td>\n",
       "      <td>0.020310</td>\n",
       "      <td>0.161900</td>\n",
       "      <td>0.057700</td>\n",
       "      <td>...</td>\n",
       "      <td>21.080000</td>\n",
       "      <td>84.110000</td>\n",
       "      <td>515.300000</td>\n",
       "      <td>0.116600</td>\n",
       "      <td>0.147200</td>\n",
       "      <td>0.114500</td>\n",
       "      <td>0.064930</td>\n",
       "      <td>0.250400</td>\n",
       "      <td>0.071460</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>13.370000</td>\n",
       "      <td>18.840000</td>\n",
       "      <td>86.240000</td>\n",
       "      <td>551.100000</td>\n",
       "      <td>0.095870</td>\n",
       "      <td>0.092630</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>0.033500</td>\n",
       "      <td>0.179200</td>\n",
       "      <td>0.061540</td>\n",
       "      <td>...</td>\n",
       "      <td>25.410000</td>\n",
       "      <td>97.660000</td>\n",
       "      <td>686.500000</td>\n",
       "      <td>0.131300</td>\n",
       "      <td>0.211900</td>\n",
       "      <td>0.226700</td>\n",
       "      <td>0.099930</td>\n",
       "      <td>0.282200</td>\n",
       "      <td>0.080040</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.780000</td>\n",
       "      <td>21.800000</td>\n",
       "      <td>104.100000</td>\n",
       "      <td>782.700000</td>\n",
       "      <td>0.105300</td>\n",
       "      <td>0.130400</td>\n",
       "      <td>0.130700</td>\n",
       "      <td>0.074000</td>\n",
       "      <td>0.195700</td>\n",
       "      <td>0.066120</td>\n",
       "      <td>...</td>\n",
       "      <td>29.720000</td>\n",
       "      <td>125.400000</td>\n",
       "      <td>1084.000000</td>\n",
       "      <td>0.146000</td>\n",
       "      <td>0.339100</td>\n",
       "      <td>0.382900</td>\n",
       "      <td>0.161400</td>\n",
       "      <td>0.317900</td>\n",
       "      <td>0.092080</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>28.110000</td>\n",
       "      <td>39.280000</td>\n",
       "      <td>188.500000</td>\n",
       "      <td>2501.000000</td>\n",
       "      <td>0.163400</td>\n",
       "      <td>0.345400</td>\n",
       "      <td>0.426800</td>\n",
       "      <td>0.201200</td>\n",
       "      <td>0.304000</td>\n",
       "      <td>0.097440</td>\n",
       "      <td>...</td>\n",
       "      <td>49.540000</td>\n",
       "      <td>251.200000</td>\n",
       "      <td>4254.000000</td>\n",
       "      <td>0.222600</td>\n",
       "      <td>1.058000</td>\n",
       "      <td>1.252000</td>\n",
       "      <td>0.291000</td>\n",
       "      <td>0.663800</td>\n",
       "      <td>0.207500</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mean radius  mean texture  mean perimeter    mean area  \\\n",
       "count   569.000000    569.000000      569.000000   569.000000   \n",
       "mean     14.127292     19.289649       91.969033   654.889104   \n",
       "std       3.524049      4.301036       24.298981   351.914129   \n",
       "min       6.981000      9.710000       43.790000   143.500000   \n",
       "25%      11.700000     16.170000       75.170000   420.300000   \n",
       "50%      13.370000     18.840000       86.240000   551.100000   \n",
       "75%      15.780000     21.800000      104.100000   782.700000   \n",
       "max      28.110000     39.280000      188.500000  2501.000000   \n",
       "\n",
       "       mean smoothness  mean compactness  mean concavity  mean concave points  \\\n",
       "count       569.000000        569.000000      569.000000           569.000000   \n",
       "mean          0.096360          0.104341        0.088799             0.048919   \n",
       "std           0.014064          0.052813        0.079720             0.038803   \n",
       "min           0.052630          0.019380        0.000000             0.000000   \n",
       "25%           0.086370          0.064920        0.029560             0.020310   \n",
       "50%           0.095870          0.092630        0.061540             0.033500   \n",
       "75%           0.105300          0.130400        0.130700             0.074000   \n",
       "max           0.163400          0.345400        0.426800             0.201200   \n",
       "\n",
       "       mean symmetry  mean fractal dimension  ...  worst texture  \\\n",
       "count     569.000000              569.000000  ...     569.000000   \n",
       "mean        0.181162                0.062798  ...      25.677223   \n",
       "std         0.027414                0.007060  ...       6.146258   \n",
       "min         0.106000                0.049960  ...      12.020000   \n",
       "25%         0.161900                0.057700  ...      21.080000   \n",
       "50%         0.179200                0.061540  ...      25.410000   \n",
       "75%         0.195700                0.066120  ...      29.720000   \n",
       "max         0.304000                0.097440  ...      49.540000   \n",
       "\n",
       "       worst perimeter   worst area  worst smoothness  worst compactness  \\\n",
       "count       569.000000   569.000000        569.000000         569.000000   \n",
       "mean        107.261213   880.583128          0.132369           0.254265   \n",
       "std          33.602542   569.356993          0.022832           0.157336   \n",
       "min          50.410000   185.200000          0.071170           0.027290   \n",
       "25%          84.110000   515.300000          0.116600           0.147200   \n",
       "50%          97.660000   686.500000          0.131300           0.211900   \n",
       "75%         125.400000  1084.000000          0.146000           0.339100   \n",
       "max         251.200000  4254.000000          0.222600           1.058000   \n",
       "\n",
       "       worst concavity  worst concave points  worst symmetry  \\\n",
       "count       569.000000            569.000000      569.000000   \n",
       "mean          0.272188              0.114606        0.290076   \n",
       "std           0.208624              0.065732        0.061867   \n",
       "min           0.000000              0.000000        0.156500   \n",
       "25%           0.114500              0.064930        0.250400   \n",
       "50%           0.226700              0.099930        0.282200   \n",
       "75%           0.382900              0.161400        0.317900   \n",
       "max           1.252000              0.291000        0.663800   \n",
       "\n",
       "       worst fractal dimension      Target  \n",
       "count               569.000000  569.000000  \n",
       "mean                  0.083946    0.627417  \n",
       "std                   0.018061    0.483918  \n",
       "min                   0.055040    0.000000  \n",
       "25%                   0.071460    0.000000  \n",
       "50%                   0.080040    1.000000  \n",
       "75%                   0.092080    1.000000  \n",
       "max                   0.207500    1.000000  \n",
       "\n",
       "[8 rows x 31 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameBreastCancer.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fc96585",
   "metadata": {},
   "source": [
    "#####  3. Remove constant columns: Remove any columns in the dataset that have constant values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "68b845f8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# To implement 3 point we need to perform Label encoding first.\n",
    "# the following Error Occur While implementing constant Column algorithm\n",
    "# ValueError: could not convert string to float: 'malignant'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90143e8e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "d3e53052",
   "metadata": {},
   "source": [
    "##### 4. Encode categorical features: If the dataset contains any categorical features, encode them using one-hot encoding or label encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "457089a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      malignant\n",
       "1      malignant\n",
       "2      malignant\n",
       "3      malignant\n",
       "4      malignant\n",
       "         ...    \n",
       "564    malignant\n",
       "565    malignant\n",
       "566    malignant\n",
       "567    malignant\n",
       "568       benign\n",
       "Name: Target Name, Length: 569, dtype: category\n",
       "Categories (2, object): ['benign', 'malignant']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrayTargetName = dataFrameBreastCancer[\"Target Name\"]\n",
    "arrayTargetName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "c94f2c96",
   "metadata": {},
   "outputs": [],
   "source": [
    "labelEncoderObject = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "73f548ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['malignant', 'benign']\n",
       "Categories (2, object): ['benign', 'malignant']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameBreastCancer[\"Target Name\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "66db84d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LabelEncoder()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LabelEncoder</label><div class=\"sk-toggleable__content\"><pre>LabelEncoder()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labelEncoderObject.fit(['malignant', 'benign'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "445363cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arrayTargetName = labelEncoderObject.transform(arrayTargetName)\n",
    "arrayTargetName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "de9b4936",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFrameBreastCancer[\"Target Name\"] = arrayTargetName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2cb8ff61",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>Target</th>\n",
       "      <th>Target Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>17.99</td>\n",
       "      <td>10.38</td>\n",
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       "      <td>1001.0</td>\n",
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       "      <td>0.27760</td>\n",
       "      <td>0.30010</td>\n",
       "      <td>0.14710</td>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.07871</td>\n",
       "      <td>...</td>\n",
       "      <td>184.60</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.66560</td>\n",
       "      <td>0.7119</td>\n",
       "      <td>0.2654</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20.57</td>\n",
       "      <td>17.77</td>\n",
       "      <td>132.90</td>\n",
       "      <td>1326.0</td>\n",
       "      <td>0.08474</td>\n",
       "      <td>0.07864</td>\n",
       "      <td>0.08690</td>\n",
       "      <td>0.07017</td>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.05667</td>\n",
       "      <td>...</td>\n",
       "      <td>158.80</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.18660</td>\n",
       "      <td>0.2416</td>\n",
       "      <td>0.1860</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19.69</td>\n",
       "      <td>21.25</td>\n",
       "      <td>130.00</td>\n",
       "      <td>1203.0</td>\n",
       "      <td>0.10960</td>\n",
       "      <td>0.15990</td>\n",
       "      <td>0.19740</td>\n",
       "      <td>0.12790</td>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.05999</td>\n",
       "      <td>...</td>\n",
       "      <td>152.50</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.42450</td>\n",
       "      <td>0.4504</td>\n",
       "      <td>0.2430</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11.42</td>\n",
       "      <td>20.38</td>\n",
       "      <td>77.58</td>\n",
       "      <td>386.1</td>\n",
       "      <td>0.14250</td>\n",
       "      <td>0.28390</td>\n",
       "      <td>0.24140</td>\n",
       "      <td>0.10520</td>\n",
       "      <td>0.2597</td>\n",
       "      <td>0.09744</td>\n",
       "      <td>...</td>\n",
       "      <td>98.87</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.86630</td>\n",
       "      <td>0.6869</td>\n",
       "      <td>0.2575</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20.29</td>\n",
       "      <td>14.34</td>\n",
       "      <td>135.10</td>\n",
       "      <td>1297.0</td>\n",
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       "      <td>0.13280</td>\n",
       "      <td>0.19800</td>\n",
       "      <td>0.10430</td>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.05883</td>\n",
       "      <td>...</td>\n",
       "      <td>152.20</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.20500</td>\n",
       "      <td>0.4000</td>\n",
       "      <td>0.1625</td>\n",
       "      <td>0.2364</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>564</th>\n",
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       "      <td>0.13890</td>\n",
       "      <td>0.1726</td>\n",
       "      <td>0.05623</td>\n",
       "      <td>...</td>\n",
       "      <td>166.10</td>\n",
       "      <td>2027.0</td>\n",
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       "      <td>0.21130</td>\n",
       "      <td>0.4107</td>\n",
       "      <td>0.2216</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>20.13</td>\n",
       "      <td>28.25</td>\n",
       "      <td>131.20</td>\n",
       "      <td>1261.0</td>\n",
       "      <td>0.09780</td>\n",
       "      <td>0.10340</td>\n",
       "      <td>0.14400</td>\n",
       "      <td>0.09791</td>\n",
       "      <td>0.1752</td>\n",
       "      <td>0.05533</td>\n",
       "      <td>...</td>\n",
       "      <td>155.00</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.19220</td>\n",
       "      <td>0.3215</td>\n",
       "      <td>0.1628</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>16.60</td>\n",
       "      <td>28.08</td>\n",
       "      <td>108.30</td>\n",
       "      <td>858.1</td>\n",
       "      <td>0.08455</td>\n",
       "      <td>0.10230</td>\n",
       "      <td>0.09251</td>\n",
       "      <td>0.05302</td>\n",
       "      <td>0.1590</td>\n",
       "      <td>0.05648</td>\n",
       "      <td>...</td>\n",
       "      <td>126.70</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.30940</td>\n",
       "      <td>0.3403</td>\n",
       "      <td>0.1418</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>20.60</td>\n",
       "      <td>29.33</td>\n",
       "      <td>140.10</td>\n",
       "      <td>1265.0</td>\n",
       "      <td>0.11780</td>\n",
       "      <td>0.27700</td>\n",
       "      <td>0.35140</td>\n",
       "      <td>0.15200</td>\n",
       "      <td>0.2397</td>\n",
       "      <td>0.07016</td>\n",
       "      <td>...</td>\n",
       "      <td>184.60</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.86810</td>\n",
       "      <td>0.9387</td>\n",
       "      <td>0.2650</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>7.76</td>\n",
       "      <td>24.54</td>\n",
       "      <td>47.92</td>\n",
       "      <td>181.0</td>\n",
       "      <td>0.05263</td>\n",
       "      <td>0.04362</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.1587</td>\n",
       "      <td>0.05884</td>\n",
       "      <td>...</td>\n",
       "      <td>59.16</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.06444</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean radius  mean texture  mean perimeter  mean area  mean smoothness  \\\n",
       "0          17.99         10.38          122.80     1001.0          0.11840   \n",
       "1          20.57         17.77          132.90     1326.0          0.08474   \n",
       "2          19.69         21.25          130.00     1203.0          0.10960   \n",
       "3          11.42         20.38           77.58      386.1          0.14250   \n",
       "4          20.29         14.34          135.10     1297.0          0.10030   \n",
       "..           ...           ...             ...        ...              ...   \n",
       "564        21.56         22.39          142.00     1479.0          0.11100   \n",
       "565        20.13         28.25          131.20     1261.0          0.09780   \n",
       "566        16.60         28.08          108.30      858.1          0.08455   \n",
       "567        20.60         29.33          140.10     1265.0          0.11780   \n",
       "568         7.76         24.54           47.92      181.0          0.05263   \n",
       "\n",
       "     mean compactness  mean concavity  mean concave points  mean symmetry  \\\n",
       "0             0.27760         0.30010              0.14710         0.2419   \n",
       "1             0.07864         0.08690              0.07017         0.1812   \n",
       "2             0.15990         0.19740              0.12790         0.2069   \n",
       "3             0.28390         0.24140              0.10520         0.2597   \n",
       "4             0.13280         0.19800              0.10430         0.1809   \n",
       "..                ...             ...                  ...            ...   \n",
       "564           0.11590         0.24390              0.13890         0.1726   \n",
       "565           0.10340         0.14400              0.09791         0.1752   \n",
       "566           0.10230         0.09251              0.05302         0.1590   \n",
       "567           0.27700         0.35140              0.15200         0.2397   \n",
       "568           0.04362         0.00000              0.00000         0.1587   \n",
       "\n",
       "     mean fractal dimension  ...  worst perimeter  worst area  \\\n",
       "0                   0.07871  ...           184.60      2019.0   \n",
       "1                   0.05667  ...           158.80      1956.0   \n",
       "2                   0.05999  ...           152.50      1709.0   \n",
       "3                   0.09744  ...            98.87       567.7   \n",
       "4                   0.05883  ...           152.20      1575.0   \n",
       "..                      ...  ...              ...         ...   \n",
       "564                 0.05623  ...           166.10      2027.0   \n",
       "565                 0.05533  ...           155.00      1731.0   \n",
       "566                 0.05648  ...           126.70      1124.0   \n",
       "567                 0.07016  ...           184.60      1821.0   \n",
       "568                 0.05884  ...            59.16       268.6   \n",
       "\n",
       "     worst smoothness  worst compactness  worst concavity  \\\n",
       "0             0.16220            0.66560           0.7119   \n",
       "1             0.12380            0.18660           0.2416   \n",
       "2             0.14440            0.42450           0.4504   \n",
       "3             0.20980            0.86630           0.6869   \n",
       "4             0.13740            0.20500           0.4000   \n",
       "..                ...                ...              ...   \n",
       "564           0.14100            0.21130           0.4107   \n",
       "565           0.11660            0.19220           0.3215   \n",
       "566           0.11390            0.30940           0.3403   \n",
       "567           0.16500            0.86810           0.9387   \n",
       "568           0.08996            0.06444           0.0000   \n",
       "\n",
       "     worst concave points  worst symmetry  worst fractal dimension  Target  \\\n",
       "0                  0.2654          0.4601                  0.11890       0   \n",
       "1                  0.1860          0.2750                  0.08902       0   \n",
       "2                  0.2430          0.3613                  0.08758       0   \n",
       "3                  0.2575          0.6638                  0.17300       0   \n",
       "4                  0.1625          0.2364                  0.07678       0   \n",
       "..                    ...             ...                      ...     ...   \n",
       "564                0.2216          0.2060                  0.07115       0   \n",
       "565                0.1628          0.2572                  0.06637       0   \n",
       "566                0.1418          0.2218                  0.07820       0   \n",
       "567                0.2650          0.4087                  0.12400       0   \n",
       "568                0.0000          0.2871                  0.07039       1   \n",
       "\n",
       "     Target Name  \n",
       "0              1  \n",
       "1              1  \n",
       "2              1  \n",
       "3              1  \n",
       "4              1  \n",
       "..           ...  \n",
       "564            1  \n",
       "565            1  \n",
       "566            1  \n",
       "567            1  \n",
       "568            0  \n",
       "\n",
       "[569 rows x 32 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameBreastCancer"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b185fb7d",
   "metadata": {},
   "source": [
    "##### Remove constant columns: Remove any columns in the dataset that have constant values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "6d723d97",
   "metadata": {},
   "outputs": [],
   "source": [
    "varThObj = VarianceThreshold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "2d4c6c3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>VarianceThreshold()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">VarianceThreshold</label><div class=\"sk-toggleable__content\"><pre>VarianceThreshold()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "VarianceThreshold()"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "varThObj.fit(dataFrameBreastCancer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e5ba7e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "        True,  True,  True,  True,  True])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "varThObj.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "e76b9a41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([], dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameBreastCancer.columns[varThObj.get_support() == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cd5257c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "### No column has variance threshold value as False,\n",
    "### thus this dataset does not cantain any constant column...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd084de0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "bc168e3b",
   "metadata": {},
   "source": [
    "#####  5. Feature scaling: Scale the numerical features using scikit-learn's StandardScaler or MinMaxScaler function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "81185b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Standard Scaler scale the dataset with distribution with 0 mean value and variance equal to 1.\n",
    "stdScalerObj = StandardScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c40a8e6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>StandardScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">StandardScaler</label><div class=\"sk-toggleable__content\"><pre>StandardScaler()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "StandardScaler()"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stdScalerObj.fit(dataFrameBreastCancer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2b399fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaledDataFrame = stdScalerObj.transform(dataFrameBreastCancer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a4dedd1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.09706398, -2.07333501,  1.26993369, ...,  1.93701461,\n",
       "        -1.29767572,  1.29767572],\n",
       "       [ 1.82982061, -0.35363241,  1.68595471, ...,  0.28118999,\n",
       "        -1.29767572,  1.29767572],\n",
       "       [ 1.57988811,  0.45618695,  1.56650313, ...,  0.20139121,\n",
       "        -1.29767572,  1.29767572],\n",
       "       ...,\n",
       "       [ 0.70228425,  2.0455738 ,  0.67267578, ..., -0.31840916,\n",
       "        -1.29767572,  1.29767572],\n",
       "       [ 1.83834103,  2.33645719,  1.98252415, ...,  2.21963528,\n",
       "        -1.29767572,  1.29767572],\n",
       "       [-1.80840125,  1.22179204, -1.81438851, ..., -0.75120669,\n",
       "         0.77060855, -0.77060855]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaledDataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c41e24a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "da42fb62",
   "metadata": {},
   "source": [
    "#####  6. Correlation analysis: Use seaborn's heatmap() function to plot a heatmap of the correlation matrix between the features. Identify highly correlated features and remove one of them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "40dc928c",
   "metadata": {},
   "outputs": [],
   "source": [
    "corrMatrix = dataFrameBreastCancer.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3303f24a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "        vertical-align: top;\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean radius</th>\n",
       "      <th>mean texture</th>\n",
       "      <th>mean perimeter</th>\n",
       "      <th>mean area</th>\n",
       "      <th>mean smoothness</th>\n",
       "      <th>mean compactness</th>\n",
       "      <th>mean concavity</th>\n",
       "      <th>mean concave points</th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <th>...</th>\n",
       "      <th>worst perimeter</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst compactness</th>\n",
       "      <th>worst concavity</th>\n",
       "      <th>worst concave points</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>Target</th>\n",
       "      <th>Target Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean radius</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.323782</td>\n",
       "      <td>0.997855</td>\n",
       "      <td>0.987357</td>\n",
       "      <td>0.170581</td>\n",
       "      <td>0.506124</td>\n",
       "      <td>0.676764</td>\n",
       "      <td>0.822529</td>\n",
       "      <td>0.147741</td>\n",
       "      <td>-0.311631</td>\n",
       "      <td>...</td>\n",
       "      <td>0.965137</td>\n",
       "      <td>0.941082</td>\n",
       "      <td>0.119616</td>\n",
       "      <td>0.413463</td>\n",
       "      <td>0.526911</td>\n",
       "      <td>0.744214</td>\n",
       "      <td>0.163953</td>\n",
       "      <td>0.007066</td>\n",
       "      <td>-0.730029</td>\n",
       "      <td>0.730029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean texture</th>\n",
       "      <td>0.323782</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.329533</td>\n",
       "      <td>0.321086</td>\n",
       "      <td>-0.023389</td>\n",
       "      <td>0.236702</td>\n",
       "      <td>0.302418</td>\n",
       "      <td>0.293464</td>\n",
       "      <td>0.071401</td>\n",
       "      <td>-0.076437</td>\n",
       "      <td>...</td>\n",
       "      <td>0.358040</td>\n",
       "      <td>0.343546</td>\n",
       "      <td>0.077503</td>\n",
       "      <td>0.277830</td>\n",
       "      <td>0.301025</td>\n",
       "      <td>0.295316</td>\n",
       "      <td>0.105008</td>\n",
       "      <td>0.119205</td>\n",
       "      <td>-0.415185</td>\n",
       "      <td>0.415185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean perimeter</th>\n",
       "      <td>0.997855</td>\n",
       "      <td>0.329533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.986507</td>\n",
       "      <td>0.207278</td>\n",
       "      <td>0.556936</td>\n",
       "      <td>0.716136</td>\n",
       "      <td>0.850977</td>\n",
       "      <td>0.183027</td>\n",
       "      <td>-0.261477</td>\n",
       "      <td>...</td>\n",
       "      <td>0.970387</td>\n",
       "      <td>0.941550</td>\n",
       "      <td>0.150549</td>\n",
       "      <td>0.455774</td>\n",
       "      <td>0.563879</td>\n",
       "      <td>0.771241</td>\n",
       "      <td>0.189115</td>\n",
       "      <td>0.051019</td>\n",
       "      <td>-0.742636</td>\n",
       "      <td>0.742636</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean area</th>\n",
       "      <td>0.987357</td>\n",
       "      <td>0.321086</td>\n",
       "      <td>0.986507</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.177028</td>\n",
       "      <td>0.498502</td>\n",
       "      <td>0.685983</td>\n",
       "      <td>0.823269</td>\n",
       "      <td>0.151293</td>\n",
       "      <td>-0.283110</td>\n",
       "      <td>...</td>\n",
       "      <td>0.959120</td>\n",
       "      <td>0.959213</td>\n",
       "      <td>0.123523</td>\n",
       "      <td>0.390410</td>\n",
       "      <td>0.512606</td>\n",
       "      <td>0.722017</td>\n",
       "      <td>0.143570</td>\n",
       "      <td>0.003738</td>\n",
       "      <td>-0.708984</td>\n",
       "      <td>0.708984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean smoothness</th>\n",
       "      <td>0.170581</td>\n",
       "      <td>-0.023389</td>\n",
       "      <td>0.207278</td>\n",
       "      <td>0.177028</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.659123</td>\n",
       "      <td>0.521984</td>\n",
       "      <td>0.553695</td>\n",
       "      <td>0.557775</td>\n",
       "      <td>0.584792</td>\n",
       "      <td>...</td>\n",
       "      <td>0.238853</td>\n",
       "      <td>0.206718</td>\n",
       "      <td>0.805324</td>\n",
       "      <td>0.472468</td>\n",
       "      <td>0.434926</td>\n",
       "      <td>0.503053</td>\n",
       "      <td>0.394309</td>\n",
       "      <td>0.499316</td>\n",
       "      <td>-0.358560</td>\n",
       "      <td>0.358560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean compactness</th>\n",
       "      <td>0.506124</td>\n",
       "      <td>0.236702</td>\n",
       "      <td>0.556936</td>\n",
       "      <td>0.498502</td>\n",
       "      <td>0.659123</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.883121</td>\n",
       "      <td>0.831135</td>\n",
       "      <td>0.602641</td>\n",
       "      <td>0.565369</td>\n",
       "      <td>...</td>\n",
       "      <td>0.590210</td>\n",
       "      <td>0.509604</td>\n",
       "      <td>0.565541</td>\n",
       "      <td>0.865809</td>\n",
       "      <td>0.816275</td>\n",
       "      <td>0.815573</td>\n",
       "      <td>0.510223</td>\n",
       "      <td>0.687382</td>\n",
       "      <td>-0.596534</td>\n",
       "      <td>0.596534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean concavity</th>\n",
       "      <td>0.676764</td>\n",
       "      <td>0.302418</td>\n",
       "      <td>0.716136</td>\n",
       "      <td>0.685983</td>\n",
       "      <td>0.521984</td>\n",
       "      <td>0.883121</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.921391</td>\n",
       "      <td>0.500667</td>\n",
       "      <td>0.336783</td>\n",
       "      <td>...</td>\n",
       "      <td>0.729565</td>\n",
       "      <td>0.675987</td>\n",
       "      <td>0.448822</td>\n",
       "      <td>0.754968</td>\n",
       "      <td>0.884103</td>\n",
       "      <td>0.861323</td>\n",
       "      <td>0.409464</td>\n",
       "      <td>0.514930</td>\n",
       "      <td>-0.696360</td>\n",
       "      <td>0.696360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean concave points</th>\n",
       "      <td>0.822529</td>\n",
       "      <td>0.293464</td>\n",
       "      <td>0.850977</td>\n",
       "      <td>0.823269</td>\n",
       "      <td>0.553695</td>\n",
       "      <td>0.831135</td>\n",
       "      <td>0.921391</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.462497</td>\n",
       "      <td>0.166917</td>\n",
       "      <td>...</td>\n",
       "      <td>0.855923</td>\n",
       "      <td>0.809630</td>\n",
       "      <td>0.452753</td>\n",
       "      <td>0.667454</td>\n",
       "      <td>0.752399</td>\n",
       "      <td>0.910155</td>\n",
       "      <td>0.375744</td>\n",
       "      <td>0.368661</td>\n",
       "      <td>-0.776614</td>\n",
       "      <td>0.776614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean symmetry</th>\n",
       "      <td>0.147741</td>\n",
       "      <td>0.071401</td>\n",
       "      <td>0.183027</td>\n",
       "      <td>0.151293</td>\n",
       "      <td>0.557775</td>\n",
       "      <td>0.602641</td>\n",
       "      <td>0.500667</td>\n",
       "      <td>0.462497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.479921</td>\n",
       "      <td>...</td>\n",
       "      <td>0.219169</td>\n",
       "      <td>0.177193</td>\n",
       "      <td>0.426675</td>\n",
       "      <td>0.473200</td>\n",
       "      <td>0.433721</td>\n",
       "      <td>0.430297</td>\n",
       "      <td>0.699826</td>\n",
       "      <td>0.438413</td>\n",
       "      <td>-0.330499</td>\n",
       "      <td>0.330499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean fractal dimension</th>\n",
       "      <td>-0.311631</td>\n",
       "      <td>-0.076437</td>\n",
       "      <td>-0.261477</td>\n",
       "      <td>-0.283110</td>\n",
       "      <td>0.584792</td>\n",
       "      <td>0.565369</td>\n",
       "      <td>0.336783</td>\n",
       "      <td>0.166917</td>\n",
       "      <td>0.479921</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.205151</td>\n",
       "      <td>-0.231854</td>\n",
       "      <td>0.504942</td>\n",
       "      <td>0.458798</td>\n",
       "      <td>0.346234</td>\n",
       "      <td>0.175325</td>\n",
       "      <td>0.334019</td>\n",
       "      <td>0.767297</td>\n",
       "      <td>0.012838</td>\n",
       "      <td>-0.012838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>radius error</th>\n",
       "      <td>0.679090</td>\n",
       "      <td>0.275869</td>\n",
       "      <td>0.691765</td>\n",
       "      <td>0.732562</td>\n",
       "      <td>0.301467</td>\n",
       "      <td>0.497473</td>\n",
       "      <td>0.631925</td>\n",
       "      <td>0.698050</td>\n",
       "      <td>0.303379</td>\n",
       "      <td>0.000111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.719684</td>\n",
       "      <td>0.751548</td>\n",
       "      <td>0.141919</td>\n",
       "      <td>0.287103</td>\n",
       "      <td>0.380585</td>\n",
       "      <td>0.531062</td>\n",
       "      <td>0.094543</td>\n",
       "      <td>0.049559</td>\n",
       "      <td>-0.567134</td>\n",
       "      <td>0.567134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texture error</th>\n",
       "      <td>-0.097317</td>\n",
       "      <td>0.386358</td>\n",
       "      <td>-0.086761</td>\n",
       "      <td>-0.066280</td>\n",
       "      <td>0.068406</td>\n",
       "      <td>0.046205</td>\n",
       "      <td>0.076218</td>\n",
       "      <td>0.021480</td>\n",
       "      <td>0.128053</td>\n",
       "      <td>0.164174</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.102242</td>\n",
       "      <td>-0.083195</td>\n",
       "      <td>-0.073658</td>\n",
       "      <td>-0.092439</td>\n",
       "      <td>-0.068956</td>\n",
       "      <td>-0.119638</td>\n",
       "      <td>-0.128215</td>\n",
       "      <td>-0.045655</td>\n",
       "      <td>0.008303</td>\n",
       "      <td>-0.008303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>perimeter error</th>\n",
       "      <td>0.674172</td>\n",
       "      <td>0.281673</td>\n",
       "      <td>0.693135</td>\n",
       "      <td>0.726628</td>\n",
       "      <td>0.296092</td>\n",
       "      <td>0.548905</td>\n",
       "      <td>0.660391</td>\n",
       "      <td>0.710650</td>\n",
       "      <td>0.313893</td>\n",
       "      <td>0.039830</td>\n",
       "      <td>...</td>\n",
       "      <td>0.721031</td>\n",
       "      <td>0.730713</td>\n",
       "      <td>0.130054</td>\n",
       "      <td>0.341919</td>\n",
       "      <td>0.418899</td>\n",
       "      <td>0.554897</td>\n",
       "      <td>0.109930</td>\n",
       "      <td>0.085433</td>\n",
       "      <td>-0.556141</td>\n",
       "      <td>0.556141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>area error</th>\n",
       "      <td>0.735864</td>\n",
       "      <td>0.259845</td>\n",
       "      <td>0.744983</td>\n",
       "      <td>0.800086</td>\n",
       "      <td>0.246552</td>\n",
       "      <td>0.455653</td>\n",
       "      <td>0.617427</td>\n",
       "      <td>0.690299</td>\n",
       "      <td>0.223970</td>\n",
       "      <td>-0.090170</td>\n",
       "      <td>...</td>\n",
       "      <td>0.761213</td>\n",
       "      <td>0.811408</td>\n",
       "      <td>0.125389</td>\n",
       "      <td>0.283257</td>\n",
       "      <td>0.385100</td>\n",
       "      <td>0.538166</td>\n",
       "      <td>0.074126</td>\n",
       "      <td>0.017539</td>\n",
       "      <td>-0.548236</td>\n",
       "      <td>0.548236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smoothness error</th>\n",
       "      <td>-0.222600</td>\n",
       "      <td>0.006614</td>\n",
       "      <td>-0.202694</td>\n",
       "      <td>-0.166777</td>\n",
       "      <td>0.332375</td>\n",
       "      <td>0.135299</td>\n",
       "      <td>0.098564</td>\n",
       "      <td>0.027653</td>\n",
       "      <td>0.187321</td>\n",
       "      <td>0.401964</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.217304</td>\n",
       "      <td>-0.182195</td>\n",
       "      <td>0.314457</td>\n",
       "      <td>-0.055558</td>\n",
       "      <td>-0.058298</td>\n",
       "      <td>-0.102007</td>\n",
       "      <td>-0.107342</td>\n",
       "      <td>0.101480</td>\n",
       "      <td>0.067016</td>\n",
       "      <td>-0.067016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>compactness error</th>\n",
       "      <td>0.206000</td>\n",
       "      <td>0.191975</td>\n",
       "      <td>0.250744</td>\n",
       "      <td>0.212583</td>\n",
       "      <td>0.318943</td>\n",
       "      <td>0.738722</td>\n",
       "      <td>0.670279</td>\n",
       "      <td>0.490424</td>\n",
       "      <td>0.421659</td>\n",
       "      <td>0.559837</td>\n",
       "      <td>...</td>\n",
       "      <td>0.260516</td>\n",
       "      <td>0.199371</td>\n",
       "      <td>0.227394</td>\n",
       "      <td>0.678780</td>\n",
       "      <td>0.639147</td>\n",
       "      <td>0.483208</td>\n",
       "      <td>0.277878</td>\n",
       "      <td>0.590973</td>\n",
       "      <td>-0.292999</td>\n",
       "      <td>0.292999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concavity error</th>\n",
       "      <td>0.194204</td>\n",
       "      <td>0.143293</td>\n",
       "      <td>0.228082</td>\n",
       "      <td>0.207660</td>\n",
       "      <td>0.248396</td>\n",
       "      <td>0.570517</td>\n",
       "      <td>0.691270</td>\n",
       "      <td>0.439167</td>\n",
       "      <td>0.342627</td>\n",
       "      <td>0.446630</td>\n",
       "      <td>...</td>\n",
       "      <td>0.226680</td>\n",
       "      <td>0.188353</td>\n",
       "      <td>0.168481</td>\n",
       "      <td>0.484858</td>\n",
       "      <td>0.662564</td>\n",
       "      <td>0.440472</td>\n",
       "      <td>0.197788</td>\n",
       "      <td>0.439329</td>\n",
       "      <td>-0.253730</td>\n",
       "      <td>0.253730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>concave points error</th>\n",
       "      <td>0.376169</td>\n",
       "      <td>0.163851</td>\n",
       "      <td>0.407217</td>\n",
       "      <td>0.372320</td>\n",
       "      <td>0.380676</td>\n",
       "      <td>0.642262</td>\n",
       "      <td>0.683260</td>\n",
       "      <td>0.615634</td>\n",
       "      <td>0.393298</td>\n",
       "      <td>0.341198</td>\n",
       "      <td>...</td>\n",
       "      <td>0.394999</td>\n",
       "      <td>0.342271</td>\n",
       "      <td>0.215351</td>\n",
       "      <td>0.452888</td>\n",
       "      <td>0.549592</td>\n",
       "      <td>0.602450</td>\n",
       "      <td>0.143116</td>\n",
       "      <td>0.310655</td>\n",
       "      <td>-0.408042</td>\n",
       "      <td>0.408042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>symmetry error</th>\n",
       "      <td>-0.104321</td>\n",
       "      <td>0.009127</td>\n",
       "      <td>-0.081629</td>\n",
       "      <td>-0.072497</td>\n",
       "      <td>0.200774</td>\n",
       "      <td>0.229977</td>\n",
       "      <td>0.178009</td>\n",
       "      <td>0.095351</td>\n",
       "      <td>0.449137</td>\n",
       "      <td>0.345007</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.103753</td>\n",
       "      <td>-0.110343</td>\n",
       "      <td>-0.012662</td>\n",
       "      <td>0.060255</td>\n",
       "      <td>0.037119</td>\n",
       "      <td>-0.030413</td>\n",
       "      <td>0.389402</td>\n",
       "      <td>0.078079</td>\n",
       "      <td>0.006522</td>\n",
       "      <td>-0.006522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fractal dimension error</th>\n",
       "      <td>-0.042641</td>\n",
       "      <td>0.054458</td>\n",
       "      <td>-0.005523</td>\n",
       "      <td>-0.019887</td>\n",
       "      <td>0.283607</td>\n",
       "      <td>0.507318</td>\n",
       "      <td>0.449301</td>\n",
       "      <td>0.257584</td>\n",
       "      <td>0.331786</td>\n",
       "      <td>0.688132</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001000</td>\n",
       "      <td>-0.022736</td>\n",
       "      <td>0.170568</td>\n",
       "      <td>0.390159</td>\n",
       "      <td>0.379975</td>\n",
       "      <td>0.215204</td>\n",
       "      <td>0.111094</td>\n",
       "      <td>0.591328</td>\n",
       "      <td>-0.077972</td>\n",
       "      <td>0.077972</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst radius</th>\n",
       "      <td>0.969539</td>\n",
       "      <td>0.352573</td>\n",
       "      <td>0.969476</td>\n",
       "      <td>0.962746</td>\n",
       "      <td>0.213120</td>\n",
       "      <td>0.535315</td>\n",
       "      <td>0.688236</td>\n",
       "      <td>0.830318</td>\n",
       "      <td>0.185728</td>\n",
       "      <td>-0.253691</td>\n",
       "      <td>...</td>\n",
       "      <td>0.993708</td>\n",
       "      <td>0.984015</td>\n",
       "      <td>0.216574</td>\n",
       "      <td>0.475820</td>\n",
       "      <td>0.573975</td>\n",
       "      <td>0.787424</td>\n",
       "      <td>0.243529</td>\n",
       "      <td>0.093492</td>\n",
       "      <td>-0.776454</td>\n",
       "      <td>0.776454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst texture</th>\n",
       "      <td>0.297008</td>\n",
       "      <td>0.912045</td>\n",
       "      <td>0.303038</td>\n",
       "      <td>0.287489</td>\n",
       "      <td>0.036072</td>\n",
       "      <td>0.248133</td>\n",
       "      <td>0.299879</td>\n",
       "      <td>0.292752</td>\n",
       "      <td>0.090651</td>\n",
       "      <td>-0.051269</td>\n",
       "      <td>...</td>\n",
       "      <td>0.365098</td>\n",
       "      <td>0.345842</td>\n",
       "      <td>0.225429</td>\n",
       "      <td>0.360832</td>\n",
       "      <td>0.368366</td>\n",
       "      <td>0.359755</td>\n",
       "      <td>0.233027</td>\n",
       "      <td>0.219122</td>\n",
       "      <td>-0.456903</td>\n",
       "      <td>0.456903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst perimeter</th>\n",
       "      <td>0.965137</td>\n",
       "      <td>0.358040</td>\n",
       "      <td>0.970387</td>\n",
       "      <td>0.959120</td>\n",
       "      <td>0.238853</td>\n",
       "      <td>0.590210</td>\n",
       "      <td>0.729565</td>\n",
       "      <td>0.855923</td>\n",
       "      <td>0.219169</td>\n",
       "      <td>-0.205151</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>0.236775</td>\n",
       "      <td>0.529408</td>\n",
       "      <td>0.618344</td>\n",
       "      <td>0.816322</td>\n",
       "      <td>0.269493</td>\n",
       "      <td>0.138957</td>\n",
       "      <td>-0.782914</td>\n",
       "      <td>0.782914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst area</th>\n",
       "      <td>0.941082</td>\n",
       "      <td>0.343546</td>\n",
       "      <td>0.941550</td>\n",
       "      <td>0.959213</td>\n",
       "      <td>0.206718</td>\n",
       "      <td>0.509604</td>\n",
       "      <td>0.675987</td>\n",
       "      <td>0.809630</td>\n",
       "      <td>0.177193</td>\n",
       "      <td>-0.231854</td>\n",
       "      <td>...</td>\n",
       "      <td>0.977578</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.209145</td>\n",
       "      <td>0.438296</td>\n",
       "      <td>0.543331</td>\n",
       "      <td>0.747419</td>\n",
       "      <td>0.209146</td>\n",
       "      <td>0.079647</td>\n",
       "      <td>-0.733825</td>\n",
       "      <td>0.733825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst smoothness</th>\n",
       "      <td>0.119616</td>\n",
       "      <td>0.077503</td>\n",
       "      <td>0.150549</td>\n",
       "      <td>0.123523</td>\n",
       "      <td>0.805324</td>\n",
       "      <td>0.565541</td>\n",
       "      <td>0.448822</td>\n",
       "      <td>0.452753</td>\n",
       "      <td>0.426675</td>\n",
       "      <td>0.504942</td>\n",
       "      <td>...</td>\n",
       "      <td>0.236775</td>\n",
       "      <td>0.209145</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.568187</td>\n",
       "      <td>0.518523</td>\n",
       "      <td>0.547691</td>\n",
       "      <td>0.493838</td>\n",
       "      <td>0.617624</td>\n",
       "      <td>-0.421465</td>\n",
       "      <td>0.421465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst compactness</th>\n",
       "      <td>0.413463</td>\n",
       "      <td>0.277830</td>\n",
       "      <td>0.455774</td>\n",
       "      <td>0.390410</td>\n",
       "      <td>0.472468</td>\n",
       "      <td>0.865809</td>\n",
       "      <td>0.754968</td>\n",
       "      <td>0.667454</td>\n",
       "      <td>0.473200</td>\n",
       "      <td>0.458798</td>\n",
       "      <td>...</td>\n",
       "      <td>0.529408</td>\n",
       "      <td>0.438296</td>\n",
       "      <td>0.568187</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.892261</td>\n",
       "      <td>0.801080</td>\n",
       "      <td>0.614441</td>\n",
       "      <td>0.810455</td>\n",
       "      <td>-0.590998</td>\n",
       "      <td>0.590998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst concavity</th>\n",
       "      <td>0.526911</td>\n",
       "      <td>0.301025</td>\n",
       "      <td>0.563879</td>\n",
       "      <td>0.512606</td>\n",
       "      <td>0.434926</td>\n",
       "      <td>0.816275</td>\n",
       "      <td>0.884103</td>\n",
       "      <td>0.752399</td>\n",
       "      <td>0.433721</td>\n",
       "      <td>0.346234</td>\n",
       "      <td>...</td>\n",
       "      <td>0.618344</td>\n",
       "      <td>0.543331</td>\n",
       "      <td>0.518523</td>\n",
       "      <td>0.892261</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>0.532520</td>\n",
       "      <td>0.686511</td>\n",
       "      <td>-0.659610</td>\n",
       "      <td>0.659610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst concave points</th>\n",
       "      <td>0.744214</td>\n",
       "      <td>0.295316</td>\n",
       "      <td>0.771241</td>\n",
       "      <td>0.722017</td>\n",
       "      <td>0.503053</td>\n",
       "      <td>0.815573</td>\n",
       "      <td>0.861323</td>\n",
       "      <td>0.910155</td>\n",
       "      <td>0.430297</td>\n",
       "      <td>0.175325</td>\n",
       "      <td>...</td>\n",
       "      <td>0.816322</td>\n",
       "      <td>0.747419</td>\n",
       "      <td>0.547691</td>\n",
       "      <td>0.801080</td>\n",
       "      <td>0.855434</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.502528</td>\n",
       "      <td>0.511114</td>\n",
       "      <td>-0.793566</td>\n",
       "      <td>0.793566</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst symmetry</th>\n",
       "      <td>0.163953</td>\n",
       "      <td>0.105008</td>\n",
       "      <td>0.189115</td>\n",
       "      <td>0.143570</td>\n",
       "      <td>0.394309</td>\n",
       "      <td>0.510223</td>\n",
       "      <td>0.409464</td>\n",
       "      <td>0.375744</td>\n",
       "      <td>0.699826</td>\n",
       "      <td>0.334019</td>\n",
       "      <td>...</td>\n",
       "      <td>0.269493</td>\n",
       "      <td>0.209146</td>\n",
       "      <td>0.493838</td>\n",
       "      <td>0.614441</td>\n",
       "      <td>0.532520</td>\n",
       "      <td>0.502528</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.537848</td>\n",
       "      <td>-0.416294</td>\n",
       "      <td>0.416294</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <td>0.007066</td>\n",
       "      <td>0.119205</td>\n",
       "      <td>0.051019</td>\n",
       "      <td>0.003738</td>\n",
       "      <td>0.499316</td>\n",
       "      <td>0.687382</td>\n",
       "      <td>0.514930</td>\n",
       "      <td>0.368661</td>\n",
       "      <td>0.438413</td>\n",
       "      <td>0.767297</td>\n",
       "      <td>...</td>\n",
       "      <td>0.138957</td>\n",
       "      <td>0.079647</td>\n",
       "      <td>0.617624</td>\n",
       "      <td>0.810455</td>\n",
       "      <td>0.686511</td>\n",
       "      <td>0.511114</td>\n",
       "      <td>0.537848</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.323872</td>\n",
       "      <td>0.323872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target</th>\n",
       "      <td>-0.730029</td>\n",
       "      <td>-0.415185</td>\n",
       "      <td>-0.742636</td>\n",
       "      <td>-0.708984</td>\n",
       "      <td>-0.358560</td>\n",
       "      <td>-0.596534</td>\n",
       "      <td>-0.696360</td>\n",
       "      <td>-0.776614</td>\n",
       "      <td>-0.330499</td>\n",
       "      <td>0.012838</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.782914</td>\n",
       "      <td>-0.733825</td>\n",
       "      <td>-0.421465</td>\n",
       "      <td>-0.590998</td>\n",
       "      <td>-0.659610</td>\n",
       "      <td>-0.793566</td>\n",
       "      <td>-0.416294</td>\n",
       "      <td>-0.323872</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Target Name</th>\n",
       "      <td>0.730029</td>\n",
       "      <td>0.415185</td>\n",
       "      <td>0.742636</td>\n",
       "      <td>0.708984</td>\n",
       "      <td>0.358560</td>\n",
       "      <td>0.596534</td>\n",
       "      <td>0.696360</td>\n",
       "      <td>0.776614</td>\n",
       "      <td>0.330499</td>\n",
       "      <td>-0.012838</td>\n",
       "      <td>...</td>\n",
       "      <td>0.782914</td>\n",
       "      <td>0.733825</td>\n",
       "      <td>0.421465</td>\n",
       "      <td>0.590998</td>\n",
       "      <td>0.659610</td>\n",
       "      <td>0.793566</td>\n",
       "      <td>0.416294</td>\n",
       "      <td>0.323872</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         mean radius  mean texture  mean perimeter  mean area  \\\n",
       "mean radius                 1.000000      0.323782        0.997855   0.987357   \n",
       "mean texture                0.323782      1.000000        0.329533   0.321086   \n",
       "mean perimeter              0.997855      0.329533        1.000000   0.986507   \n",
       "mean area                   0.987357      0.321086        0.986507   1.000000   \n",
       "mean smoothness             0.170581     -0.023389        0.207278   0.177028   \n",
       "mean compactness            0.506124      0.236702        0.556936   0.498502   \n",
       "mean concavity              0.676764      0.302418        0.716136   0.685983   \n",
       "mean concave points         0.822529      0.293464        0.850977   0.823269   \n",
       "mean symmetry               0.147741      0.071401        0.183027   0.151293   \n",
       "mean fractal dimension     -0.311631     -0.076437       -0.261477  -0.283110   \n",
       "radius error                0.679090      0.275869        0.691765   0.732562   \n",
       "texture error              -0.097317      0.386358       -0.086761  -0.066280   \n",
       "perimeter error             0.674172      0.281673        0.693135   0.726628   \n",
       "area error                  0.735864      0.259845        0.744983   0.800086   \n",
       "smoothness error           -0.222600      0.006614       -0.202694  -0.166777   \n",
       "compactness error           0.206000      0.191975        0.250744   0.212583   \n",
       "concavity error             0.194204      0.143293        0.228082   0.207660   \n",
       "concave points error        0.376169      0.163851        0.407217   0.372320   \n",
       "symmetry error             -0.104321      0.009127       -0.081629  -0.072497   \n",
       "fractal dimension error    -0.042641      0.054458       -0.005523  -0.019887   \n",
       "worst radius                0.969539      0.352573        0.969476   0.962746   \n",
       "worst texture               0.297008      0.912045        0.303038   0.287489   \n",
       "worst perimeter             0.965137      0.358040        0.970387   0.959120   \n",
       "worst area                  0.941082      0.343546        0.941550   0.959213   \n",
       "worst smoothness            0.119616      0.077503        0.150549   0.123523   \n",
       "worst compactness           0.413463      0.277830        0.455774   0.390410   \n",
       "worst concavity             0.526911      0.301025        0.563879   0.512606   \n",
       "worst concave points        0.744214      0.295316        0.771241   0.722017   \n",
       "worst symmetry              0.163953      0.105008        0.189115   0.143570   \n",
       "worst fractal dimension     0.007066      0.119205        0.051019   0.003738   \n",
       "Target                     -0.730029     -0.415185       -0.742636  -0.708984   \n",
       "Target Name                 0.730029      0.415185        0.742636   0.708984   \n",
       "\n",
       "                         mean smoothness  mean compactness  mean concavity  \\\n",
       "mean radius                     0.170581          0.506124        0.676764   \n",
       "mean texture                   -0.023389          0.236702        0.302418   \n",
       "mean perimeter                  0.207278          0.556936        0.716136   \n",
       "mean area                       0.177028          0.498502        0.685983   \n",
       "mean smoothness                 1.000000          0.659123        0.521984   \n",
       "mean compactness                0.659123          1.000000        0.883121   \n",
       "mean concavity                  0.521984          0.883121        1.000000   \n",
       "mean concave points             0.553695          0.831135        0.921391   \n",
       "mean symmetry                   0.557775          0.602641        0.500667   \n",
       "mean fractal dimension          0.584792          0.565369        0.336783   \n",
       "radius error                    0.301467          0.497473        0.631925   \n",
       "texture error                   0.068406          0.046205        0.076218   \n",
       "perimeter error                 0.296092          0.548905        0.660391   \n",
       "area error                      0.246552          0.455653        0.617427   \n",
       "smoothness error                0.332375          0.135299        0.098564   \n",
       "compactness error               0.318943          0.738722        0.670279   \n",
       "concavity error                 0.248396          0.570517        0.691270   \n",
       "concave points error            0.380676          0.642262        0.683260   \n",
       "symmetry error                  0.200774          0.229977        0.178009   \n",
       "fractal dimension error         0.283607          0.507318        0.449301   \n",
       "worst radius                    0.213120          0.535315        0.688236   \n",
       "worst texture                   0.036072          0.248133        0.299879   \n",
       "worst perimeter                 0.238853          0.590210        0.729565   \n",
       "worst area                      0.206718          0.509604        0.675987   \n",
       "worst smoothness                0.805324          0.565541        0.448822   \n",
       "worst compactness               0.472468          0.865809        0.754968   \n",
       "worst concavity                 0.434926          0.816275        0.884103   \n",
       "worst concave points            0.503053          0.815573        0.861323   \n",
       "worst symmetry                  0.394309          0.510223        0.409464   \n",
       "worst fractal dimension         0.499316          0.687382        0.514930   \n",
       "Target                         -0.358560         -0.596534       -0.696360   \n",
       "Target Name                     0.358560          0.596534        0.696360   \n",
       "\n",
       "                         mean concave points  mean symmetry  \\\n",
       "mean radius                         0.822529       0.147741   \n",
       "mean texture                        0.293464       0.071401   \n",
       "mean perimeter                      0.850977       0.183027   \n",
       "mean area                           0.823269       0.151293   \n",
       "mean smoothness                     0.553695       0.557775   \n",
       "mean compactness                    0.831135       0.602641   \n",
       "mean concavity                      0.921391       0.500667   \n",
       "mean concave points                 1.000000       0.462497   \n",
       "mean symmetry                       0.462497       1.000000   \n",
       "mean fractal dimension              0.166917       0.479921   \n",
       "radius error                        0.698050       0.303379   \n",
       "texture error                       0.021480       0.128053   \n",
       "perimeter error                     0.710650       0.313893   \n",
       "area error                          0.690299       0.223970   \n",
       "smoothness error                    0.027653       0.187321   \n",
       "compactness error                   0.490424       0.421659   \n",
       "concavity error                     0.439167       0.342627   \n",
       "concave points error                0.615634       0.393298   \n",
       "symmetry error                      0.095351       0.449137   \n",
       "fractal dimension error             0.257584       0.331786   \n",
       "worst radius                        0.830318       0.185728   \n",
       "worst texture                       0.292752       0.090651   \n",
       "worst perimeter                     0.855923       0.219169   \n",
       "worst area                          0.809630       0.177193   \n",
       "worst smoothness                    0.452753       0.426675   \n",
       "worst compactness                   0.667454       0.473200   \n",
       "worst concavity                     0.752399       0.433721   \n",
       "worst concave points                0.910155       0.430297   \n",
       "worst symmetry                      0.375744       0.699826   \n",
       "worst fractal dimension             0.368661       0.438413   \n",
       "Target                             -0.776614      -0.330499   \n",
       "Target Name                         0.776614       0.330499   \n",
       "\n",
       "                         mean fractal dimension  ...  worst perimeter  \\\n",
       "mean radius                           -0.311631  ...         0.965137   \n",
       "mean texture                          -0.076437  ...         0.358040   \n",
       "mean perimeter                        -0.261477  ...         0.970387   \n",
       "mean area                             -0.283110  ...         0.959120   \n",
       "mean smoothness                        0.584792  ...         0.238853   \n",
       "mean compactness                       0.565369  ...         0.590210   \n",
       "mean concavity                         0.336783  ...         0.729565   \n",
       "mean concave points                    0.166917  ...         0.855923   \n",
       "mean symmetry                          0.479921  ...         0.219169   \n",
       "mean fractal dimension                 1.000000  ...        -0.205151   \n",
       "radius error                           0.000111  ...         0.719684   \n",
       "texture error                          0.164174  ...        -0.102242   \n",
       "perimeter error                        0.039830  ...         0.721031   \n",
       "area error                            -0.090170  ...         0.761213   \n",
       "smoothness error                       0.401964  ...        -0.217304   \n",
       "compactness error                      0.559837  ...         0.260516   \n",
       "concavity error                        0.446630  ...         0.226680   \n",
       "concave points error                   0.341198  ...         0.394999   \n",
       "symmetry error                         0.345007  ...        -0.103753   \n",
       "fractal dimension error                0.688132  ...        -0.001000   \n",
       "worst radius                          -0.253691  ...         0.993708   \n",
       "worst texture                         -0.051269  ...         0.365098   \n",
       "worst perimeter                       -0.205151  ...         1.000000   \n",
       "worst area                            -0.231854  ...         0.977578   \n",
       "worst smoothness                       0.504942  ...         0.236775   \n",
       "worst compactness                      0.458798  ...         0.529408   \n",
       "worst concavity                        0.346234  ...         0.618344   \n",
       "worst concave points                   0.175325  ...         0.816322   \n",
       "worst symmetry                         0.334019  ...         0.269493   \n",
       "worst fractal dimension                0.767297  ...         0.138957   \n",
       "Target                                 0.012838  ...        -0.782914   \n",
       "Target Name                           -0.012838  ...         0.782914   \n",
       "\n",
       "                         worst area  worst smoothness  worst compactness  \\\n",
       "mean radius                0.941082          0.119616           0.413463   \n",
       "mean texture               0.343546          0.077503           0.277830   \n",
       "mean perimeter             0.941550          0.150549           0.455774   \n",
       "mean area                  0.959213          0.123523           0.390410   \n",
       "mean smoothness            0.206718          0.805324           0.472468   \n",
       "mean compactness           0.509604          0.565541           0.865809   \n",
       "mean concavity             0.675987          0.448822           0.754968   \n",
       "mean concave points        0.809630          0.452753           0.667454   \n",
       "mean symmetry              0.177193          0.426675           0.473200   \n",
       "mean fractal dimension    -0.231854          0.504942           0.458798   \n",
       "radius error               0.751548          0.141919           0.287103   \n",
       "texture error             -0.083195         -0.073658          -0.092439   \n",
       "perimeter error            0.730713          0.130054           0.341919   \n",
       "area error                 0.811408          0.125389           0.283257   \n",
       "smoothness error          -0.182195          0.314457          -0.055558   \n",
       "compactness error          0.199371          0.227394           0.678780   \n",
       "concavity error            0.188353          0.168481           0.484858   \n",
       "concave points error       0.342271          0.215351           0.452888   \n",
       "symmetry error            -0.110343         -0.012662           0.060255   \n",
       "fractal dimension error   -0.022736          0.170568           0.390159   \n",
       "worst radius               0.984015          0.216574           0.475820   \n",
       "worst texture              0.345842          0.225429           0.360832   \n",
       "worst perimeter            0.977578          0.236775           0.529408   \n",
       "worst area                 1.000000          0.209145           0.438296   \n",
       "worst smoothness           0.209145          1.000000           0.568187   \n",
       "worst compactness          0.438296          0.568187           1.000000   \n",
       "worst concavity            0.543331          0.518523           0.892261   \n",
       "worst concave points       0.747419          0.547691           0.801080   \n",
       "worst symmetry             0.209146          0.493838           0.614441   \n",
       "worst fractal dimension    0.079647          0.617624           0.810455   \n",
       "Target                    -0.733825         -0.421465          -0.590998   \n",
       "Target Name                0.733825          0.421465           0.590998   \n",
       "\n",
       "                         worst concavity  worst concave points  \\\n",
       "mean radius                     0.526911              0.744214   \n",
       "mean texture                    0.301025              0.295316   \n",
       "mean perimeter                  0.563879              0.771241   \n",
       "mean area                       0.512606              0.722017   \n",
       "mean smoothness                 0.434926              0.503053   \n",
       "mean compactness                0.816275              0.815573   \n",
       "mean concavity                  0.884103              0.861323   \n",
       "mean concave points             0.752399              0.910155   \n",
       "mean symmetry                   0.433721              0.430297   \n",
       "mean fractal dimension          0.346234              0.175325   \n",
       "radius error                    0.380585              0.531062   \n",
       "texture error                  -0.068956             -0.119638   \n",
       "perimeter error                 0.418899              0.554897   \n",
       "area error                      0.385100              0.538166   \n",
       "smoothness error               -0.058298             -0.102007   \n",
       "compactness error               0.639147              0.483208   \n",
       "concavity error                 0.662564              0.440472   \n",
       "concave points error            0.549592              0.602450   \n",
       "symmetry error                  0.037119             -0.030413   \n",
       "fractal dimension error         0.379975              0.215204   \n",
       "worst radius                    0.573975              0.787424   \n",
       "worst texture                   0.368366              0.359755   \n",
       "worst perimeter                 0.618344              0.816322   \n",
       "worst area                      0.543331              0.747419   \n",
       "worst smoothness                0.518523              0.547691   \n",
       "worst compactness               0.892261              0.801080   \n",
       "worst concavity                 1.000000              0.855434   \n",
       "worst concave points            0.855434              1.000000   \n",
       "worst symmetry                  0.532520              0.502528   \n",
       "worst fractal dimension         0.686511              0.511114   \n",
       "Target                         -0.659610             -0.793566   \n",
       "Target Name                     0.659610              0.793566   \n",
       "\n",
       "                         worst symmetry  worst fractal dimension    Target  \\\n",
       "mean radius                    0.163953                 0.007066 -0.730029   \n",
       "mean texture                   0.105008                 0.119205 -0.415185   \n",
       "mean perimeter                 0.189115                 0.051019 -0.742636   \n",
       "mean area                      0.143570                 0.003738 -0.708984   \n",
       "mean smoothness                0.394309                 0.499316 -0.358560   \n",
       "mean compactness               0.510223                 0.687382 -0.596534   \n",
       "mean concavity                 0.409464                 0.514930 -0.696360   \n",
       "mean concave points            0.375744                 0.368661 -0.776614   \n",
       "mean symmetry                  0.699826                 0.438413 -0.330499   \n",
       "mean fractal dimension         0.334019                 0.767297  0.012838   \n",
       "radius error                   0.094543                 0.049559 -0.567134   \n",
       "texture error                 -0.128215                -0.045655  0.008303   \n",
       "perimeter error                0.109930                 0.085433 -0.556141   \n",
       "area error                     0.074126                 0.017539 -0.548236   \n",
       "smoothness error              -0.107342                 0.101480  0.067016   \n",
       "compactness error              0.277878                 0.590973 -0.292999   \n",
       "concavity error                0.197788                 0.439329 -0.253730   \n",
       "concave points error           0.143116                 0.310655 -0.408042   \n",
       "symmetry error                 0.389402                 0.078079  0.006522   \n",
       "fractal dimension error        0.111094                 0.591328 -0.077972   \n",
       "worst radius                   0.243529                 0.093492 -0.776454   \n",
       "worst texture                  0.233027                 0.219122 -0.456903   \n",
       "worst perimeter                0.269493                 0.138957 -0.782914   \n",
       "worst area                     0.209146                 0.079647 -0.733825   \n",
       "worst smoothness               0.493838                 0.617624 -0.421465   \n",
       "worst compactness              0.614441                 0.810455 -0.590998   \n",
       "worst concavity                0.532520                 0.686511 -0.659610   \n",
       "worst concave points           0.502528                 0.511114 -0.793566   \n",
       "worst symmetry                 1.000000                 0.537848 -0.416294   \n",
       "worst fractal dimension        0.537848                 1.000000 -0.323872   \n",
       "Target                        -0.416294                -0.323872  1.000000   \n",
       "Target Name                    0.416294                 0.323872 -1.000000   \n",
       "\n",
       "                         Target Name  \n",
       "mean radius                 0.730029  \n",
       "mean texture                0.415185  \n",
       "mean perimeter              0.742636  \n",
       "mean area                   0.708984  \n",
       "mean smoothness             0.358560  \n",
       "mean compactness            0.596534  \n",
       "mean concavity              0.696360  \n",
       "mean concave points         0.776614  \n",
       "mean symmetry               0.330499  \n",
       "mean fractal dimension     -0.012838  \n",
       "radius error                0.567134  \n",
       "texture error              -0.008303  \n",
       "perimeter error             0.556141  \n",
       "area error                  0.548236  \n",
       "smoothness error           -0.067016  \n",
       "compactness error           0.292999  \n",
       "concavity error             0.253730  \n",
       "concave points error        0.408042  \n",
       "symmetry error             -0.006522  \n",
       "fractal dimension error     0.077972  \n",
       "worst radius                0.776454  \n",
       "worst texture               0.456903  \n",
       "worst perimeter             0.782914  \n",
       "worst area                  0.733825  \n",
       "worst smoothness            0.421465  \n",
       "worst compactness           0.590998  \n",
       "worst concavity             0.659610  \n",
       "worst concave points        0.793566  \n",
       "worst symmetry              0.416294  \n",
       "worst fractal dimension     0.323872  \n",
       "Target                     -1.000000  \n",
       "Target Name                 1.000000  \n",
       "\n",
       "[32 rows x 32 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corrMatrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "ad9cd919",
   "metadata": {},
   "outputs": [],
   "source": [
    "# correlation values lies between -1 to +1\n",
    "# I have intentionally used the correlation positive_threshold and negative_threshold as .75,\n",
    "# so, that more number columns left to implement feature selction algorithm.... \n",
    "positive_threshold = .75\n",
    "negative_threshold = -.75"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bf512a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "redundantColumnsNames = set()\n",
    "for row in range(len(corrMatrix)):\n",
    "    for column in range(row):\n",
    "        if corrMatrix.iloc[row][column] > positive_threshold or corrMatrix.iloc[row][column] < negative_threshold:\n",
    "            redundantColumnsNames.add(corrMatrix.columns[column])\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "a4fc9e5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(redundantColumnsNames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "afb2164b",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFrameBreastCancer.drop(redundantColumnsNames, inplace = True, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "d6644d83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean symmetry</th>\n",
       "      <th>texture error</th>\n",
       "      <th>smoothness error</th>\n",
       "      <th>concave points error</th>\n",
       "      <th>symmetry error</th>\n",
       "      <th>fractal dimension error</th>\n",
       "      <th>worst texture</th>\n",
       "      <th>worst area</th>\n",
       "      <th>worst smoothness</th>\n",
       "      <th>worst symmetry</th>\n",
       "      <th>worst fractal dimension</th>\n",
       "      <th>Target Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.2419</td>\n",
       "      <td>0.9053</td>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.01587</td>\n",
       "      <td>0.03003</td>\n",
       "      <td>0.006193</td>\n",
       "      <td>17.33</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>0.16220</td>\n",
       "      <td>0.4601</td>\n",
       "      <td>0.11890</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1812</td>\n",
       "      <td>0.7339</td>\n",
       "      <td>0.005225</td>\n",
       "      <td>0.01340</td>\n",
       "      <td>0.01389</td>\n",
       "      <td>0.003532</td>\n",
       "      <td>23.41</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>0.12380</td>\n",
       "      <td>0.2750</td>\n",
       "      <td>0.08902</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.2069</td>\n",
       "      <td>0.7869</td>\n",
       "      <td>0.006150</td>\n",
       "      <td>0.02058</td>\n",
       "      <td>0.02250</td>\n",
       "      <td>0.004571</td>\n",
       "      <td>25.53</td>\n",
       "      <td>1709.0</td>\n",
       "      <td>0.14440</td>\n",
       "      <td>0.3613</td>\n",
       "      <td>0.08758</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.2597</td>\n",
       "      <td>1.1560</td>\n",
       "      <td>0.009110</td>\n",
       "      <td>0.01867</td>\n",
       "      <td>0.05963</td>\n",
       "      <td>0.009208</td>\n",
       "      <td>26.50</td>\n",
       "      <td>567.7</td>\n",
       "      <td>0.20980</td>\n",
       "      <td>0.6638</td>\n",
       "      <td>0.17300</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1809</td>\n",
       "      <td>0.7813</td>\n",
       "      <td>0.011490</td>\n",
       "      <td>0.01885</td>\n",
       "      <td>0.01756</td>\n",
       "      <td>0.005115</td>\n",
       "      <td>16.67</td>\n",
       "      <td>1575.0</td>\n",
       "      <td>0.13740</td>\n",
       "      <td>0.2364</td>\n",
       "      <td>0.07678</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>0.1726</td>\n",
       "      <td>1.2560</td>\n",
       "      <td>0.010300</td>\n",
       "      <td>0.02454</td>\n",
       "      <td>0.01114</td>\n",
       "      <td>0.004239</td>\n",
       "      <td>26.40</td>\n",
       "      <td>2027.0</td>\n",
       "      <td>0.14100</td>\n",
       "      <td>0.2060</td>\n",
       "      <td>0.07115</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>0.1752</td>\n",
       "      <td>2.4630</td>\n",
       "      <td>0.005769</td>\n",
       "      <td>0.01678</td>\n",
       "      <td>0.01898</td>\n",
       "      <td>0.002498</td>\n",
       "      <td>38.25</td>\n",
       "      <td>1731.0</td>\n",
       "      <td>0.11660</td>\n",
       "      <td>0.2572</td>\n",
       "      <td>0.06637</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>566</th>\n",
       "      <td>0.1590</td>\n",
       "      <td>1.0750</td>\n",
       "      <td>0.005903</td>\n",
       "      <td>0.01557</td>\n",
       "      <td>0.01318</td>\n",
       "      <td>0.003892</td>\n",
       "      <td>34.12</td>\n",
       "      <td>1124.0</td>\n",
       "      <td>0.11390</td>\n",
       "      <td>0.2218</td>\n",
       "      <td>0.07820</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>0.2397</td>\n",
       "      <td>1.5950</td>\n",
       "      <td>0.006522</td>\n",
       "      <td>0.01664</td>\n",
       "      <td>0.02324</td>\n",
       "      <td>0.006185</td>\n",
       "      <td>39.42</td>\n",
       "      <td>1821.0</td>\n",
       "      <td>0.16500</td>\n",
       "      <td>0.4087</td>\n",
       "      <td>0.12400</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>568</th>\n",
       "      <td>0.1587</td>\n",
       "      <td>1.4280</td>\n",
       "      <td>0.007189</td>\n",
       "      <td>0.00000</td>\n",
       "      <td>0.02676</td>\n",
       "      <td>0.002783</td>\n",
       "      <td>30.37</td>\n",
       "      <td>268.6</td>\n",
       "      <td>0.08996</td>\n",
       "      <td>0.2871</td>\n",
       "      <td>0.07039</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>569 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     mean symmetry  texture error  smoothness error  concave points error  \\\n",
       "0           0.2419         0.9053          0.006399               0.01587   \n",
       "1           0.1812         0.7339          0.005225               0.01340   \n",
       "2           0.2069         0.7869          0.006150               0.02058   \n",
       "3           0.2597         1.1560          0.009110               0.01867   \n",
       "4           0.1809         0.7813          0.011490               0.01885   \n",
       "..             ...            ...               ...                   ...   \n",
       "564         0.1726         1.2560          0.010300               0.02454   \n",
       "565         0.1752         2.4630          0.005769               0.01678   \n",
       "566         0.1590         1.0750          0.005903               0.01557   \n",
       "567         0.2397         1.5950          0.006522               0.01664   \n",
       "568         0.1587         1.4280          0.007189               0.00000   \n",
       "\n",
       "     symmetry error  fractal dimension error  worst texture  worst area  \\\n",
       "0           0.03003                 0.006193          17.33      2019.0   \n",
       "1           0.01389                 0.003532          23.41      1956.0   \n",
       "2           0.02250                 0.004571          25.53      1709.0   \n",
       "3           0.05963                 0.009208          26.50       567.7   \n",
       "4           0.01756                 0.005115          16.67      1575.0   \n",
       "..              ...                      ...            ...         ...   \n",
       "564         0.01114                 0.004239          26.40      2027.0   \n",
       "565         0.01898                 0.002498          38.25      1731.0   \n",
       "566         0.01318                 0.003892          34.12      1124.0   \n",
       "567         0.02324                 0.006185          39.42      1821.0   \n",
       "568         0.02676                 0.002783          30.37       268.6   \n",
       "\n",
       "     worst smoothness  worst symmetry  worst fractal dimension  Target Name  \n",
       "0             0.16220          0.4601                  0.11890            1  \n",
       "1             0.12380          0.2750                  0.08902            1  \n",
       "2             0.14440          0.3613                  0.08758            1  \n",
       "3             0.20980          0.6638                  0.17300            1  \n",
       "4             0.13740          0.2364                  0.07678            1  \n",
       "..                ...             ...                      ...          ...  \n",
       "564           0.14100          0.2060                  0.07115            1  \n",
       "565           0.11660          0.2572                  0.06637            1  \n",
       "566           0.11390          0.2218                  0.07820            1  \n",
       "567           0.16500          0.4087                  0.12400            1  \n",
       "568           0.08996          0.2871                  0.07039            0  \n",
       "\n",
       "[569 rows x 12 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameBreastCancer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "347f60d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(dataFrameBreastCancer.corr())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0da5eda4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "6b7f20dc",
   "metadata": {},
   "source": [
    "#####  7. Feature selection: Use scikit-learn's SelectKBest or SelectPercentile function to select the k best or top percentile features based on their correlation with the target variable.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6db8d7cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# I have selected the K best select feature selection algorithm to select top 4 features. \n",
    "# this algorithm work on correction values between different columns, the columns which are highly correlated \n",
    "# are removed from dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "09dfab94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['mean symmetry', 'texture error', 'smoothness error',\n",
       "       'concave points error', 'symmetry error', 'fractal dimension error',\n",
       "       'worst texture', 'worst area', 'worst smoothness', 'worst symmetry',\n",
       "       'worst fractal dimension', 'Target Name'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrameBreastCancer.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "c3a051be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split the data into 2 parts X and Y to train model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "5f35b81d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_Cancer = dataFrameBreastCancer[['mean symmetry', 'texture error', 'smoothness error',\n",
    "       'concave points error', 'symmetry error', 'fractal dimension error',\n",
    "       'worst texture', 'worst area', 'worst smoothness', 'worst symmetry',\n",
    "       'worst fractal dimension']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "077cc688",
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_Cancer = dataFrameBreastCancer['Target Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "df766037",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 11)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_Cancer.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "74c4449e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569,)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_Cancer.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "3b6e4b18",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data in training and test set\n",
    "X_train_cancer, X_test_cancer, y_train_cancer, y_test_cancer = train_test_split(\n",
    "    X_Cancer, Y_Cancer, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e5c9a4f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Features before SelectKBest ===  11\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of Features before SelectKBest === \", X_train_cancer.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5a2f0805",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SelectKBest(k=4)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SelectKBest</label><div class=\"sk-toggleable__content\"><pre>SelectKBest(k=4)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SelectKBest(k=4)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create object of selectKbest and train model\n",
    "selectKBestObj = SelectKBest(k=4) \n",
    "# Default value of score_func is f_classif,  \n",
    "# The default function only works with classification data.\n",
    "selectKBestObj.fit(X_train_cancer, y_train_cancer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5e61966d",
   "metadata": {},
   "outputs": [],
   "source": [
    "KBestCoulmnIndexVal = selectKBestObj.get_support(indices= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "8812a94e",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFrameWithNewColumns = dataFrameBreastCancer.iloc[:,KBestCoulmnIndexVal]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "5583265c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['worst texture', 'worst area', 'worst smoothness', 'worst symmetry'], dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# thus the final Dataframe after implementing KBestSelect algorithm\n",
    "dataFrameWithNewColumns.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "05814d78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "77db7838",
   "metadata": {},
   "outputs": [],
   "source": [
    "### implement Logistic Regression classification algrithm to find Accuracy of model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "c684b25d",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_cancer_new = dataFrameWithNewColumns[['worst texture', 'worst area', 'worst smoothness', 'worst symmetry']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "2ff9d93f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569, 4)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_cancer_new.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "86d42a34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(569,)"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_Cancer.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "c67c5c58",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test = train_test_split(X_train_cancer_new,Y_Cancer, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "db369c31",
   "metadata": {},
   "outputs": [],
   "source": [
    "LogisRegObject = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c56254ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LogisRegObject.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "d0c34bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_Pred = LogisRegObject.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "2e3a3d45",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.956140350877193"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_Pred,y_test) # thus the accuracy of this model is 93%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb0d6979",
   "metadata": {},
   "outputs": [],
   "source": []
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   "cell_type": "code",
   "execution_count": null,
   "id": "40be2691",
   "metadata": {},
   "outputs": [],
   "source": []
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "780d2885",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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